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Proceedings Paper

A structural framework for anomalous change detection and characterization
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Paper Abstract

We present a spatially adaptive scheme for automatically searching a pair of images of a scene for unusual and interesting changes. Our motivation is to bring into play structural aspects of image features alongside the spectral attributes used for anomalous change detection (ACD). We leverage a small but informative subset of pixels, namely edge pixels of the images, as anchor points of a Delaunay triangulation to jointly decompose the images into a set of triangular regions, called trixels, which are spectrally uniform. Such decomposition helps in image regularization by simple-function approximation on a feature-adaptive grid. Applying ACD to this trixel grid instead of pixels offers several advantages. It allows: 1) edge-preserving smoothing of images, 2) speed-up of spatial computations by significantly reducing the representation of the images, and 3) the easy recovery of structure of the detected anomalous changes by associating anomalous trixels with polygonal image features. The latter facility further enables the application of shape-theoretic criteria and algorithms to characterize the changes and recognize them as interesting or not. This incorporation of spatial information has the potential to filter out some spurious changes, such as due to parallax, shadows, and misregistration, by identifying and filtering out those that are structurally similar and spatially pervasive. Our framework supports the joint spatial and spectral analysis of images, potentially enabling the design of more robust ACD algorithms.

Paper Details

Date Published: 28 April 2009
PDF: 10 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410N (28 April 2009); doi: 10.1117/12.818977
Show Author Affiliations
Lakshman Prasad, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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